Vehicle travel path recognition in urban dense road network environments by using mobile phone data

Тип публикации: Journal Article
Дата публикации: 2021-07-16
Taylor & Francis
scimago Q1
wos Q2
БС1
SJR: 0.997
CiteScore: 8.6
Impact factor: 3.1
ISSN: 23249935, 23249943
General Engineering
Transportation
Краткое описание
Vehicle travel paths provide basic information for improving traffic forecasting models, tracking epidemics transmission, and road construction. Nevertheless, the challenge of recognition and verif...
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ГОСТ |
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ГОСТ Скопировать
Guo Y. et al. Vehicle travel path recognition in urban dense road network environments by using mobile phone data // Transportmetrica A: Transport Science. 2021. pp. 1-21.
ГОСТ со всеми авторами (до 50) Скопировать
Guo Y., Yang F., Jin P. J., Liu H., Ma S., Yao Z. Vehicle travel path recognition in urban dense road network environments by using mobile phone data // Transportmetrica A: Transport Science. 2021. pp. 1-21.
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TY - JOUR
DO - 10.1080/23249935.2021.1948931
UR - https://doi.org/10.1080/23249935.2021.1948931
TI - Vehicle travel path recognition in urban dense road network environments by using mobile phone data
T2 - Transportmetrica A: Transport Science
AU - Guo, Yudong
AU - Yang, Fei
AU - Jin, Peter J.
AU - Liu, Haode
AU - Ma, Sai
AU - Yao, Zhenxing
PY - 2021
DA - 2021/07/16
PB - Taylor & Francis
SP - 1-21
SN - 2324-9935
SN - 2324-9943
ER -
BibTex
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@article{2021_Guo,
author = {Yudong Guo and Fei Yang and Peter J. Jin and Haode Liu and Sai Ma and Zhenxing Yao},
title = {Vehicle travel path recognition in urban dense road network environments by using mobile phone data},
journal = {Transportmetrica A: Transport Science},
year = {2021},
publisher = {Taylor & Francis},
month = {jul},
url = {https://doi.org/10.1080/23249935.2021.1948931},
pages = {1--21},
doi = {10.1080/23249935.2021.1948931}
}